Determining plastic slips in rate-independent crystal plasticity models through machine learning algorithms
文献类型：期刊论文
| 作者 | Wang ZW(王志文)1,2; Chen XJ(陈贤佳)2 ; Wen JC(温济慈)1,2; Wei YJ(魏宇杰)1,2
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| 刊名 | EXTREME MECHANICS LETTERS
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| 出版日期 | 2024-09-01 |
| 卷号 | 71页码:13 |
| 关键词 | Machine learning Crystal plasticity Slip system Taylor criterion Maximum dissipation Finite element method |
| ISSN号 | 2352-4316 |
| DOI | 10.1016/j.eml.2024.102216 |
| 通讯作者 | Wei, Yujie(yujie_wei@lnm.imech.ac.cn) |
| 英文摘要 | Dislocation slip-based crystal plasticity models have been a great success in connecting the fundamental physics with the macroscopic deformation of crystalline materials. Pioneered by Taylor in his work on "plastic strain in metals" (Taylor, 1938), and further advanced by Bishop and Hill (1951a, 1951b), the Taylor-Bishop-Hill theory laid the foundation of today's constitutive models on crystal plasticity. An intriguing part of those modeling is to determine the active slip systems-which system to be involved in and how much it contributes to the deformation. In this paper, we developed a machine learning-based algorithm to determine accurately and efficiently the active slip systems in crystal plasticity constitutive models. Applications to the common three polycrystalline metals, face-centered cubic (FCC) copper, body-centered cubic (BCC) alpha-iron, and hexagonal close-packed (HCP) AZ31B, demonstrate that even a simple neural network could give rise to accurate and efficient results in comparing with traditional routines. There seems to be plenty of space for further reducing the computation time and hence scaling up the simulating samples. |
| 分类号 | 二类/Q1 |
| WOS关键词 | CRYSTALLOGRAPHIC TEXTURE ; NEURAL-NETWORKS ; DEFORMATION ; STRAIN ; EVOLUTION ; MICROMECHANICS ; METALS |
| 资助项目 | National Natural Sci-ence Foundation of China (NSFC) Basic Science Center for 'Multiscale Problems in Nonlinear Mechanics', China[11988102] |
| WOS研究方向 | Engineering ; Materials Science ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:001286480400001 |
| 资助机构 | National Natural Sci-ence Foundation of China (NSFC) Basic Science Center for 'Multiscale Problems in Nonlinear Mechanics', China |
| 其他责任者 | Wei, Yujie |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/96295]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, State Key Lab Nonlinear Mech LNM, Inst Mech, Beijing 100190, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Wang ZW,Chen XJ,Wen JC,et al. Determining plastic slips in rate-independent crystal plasticity models through machine learning algorithms[J]. EXTREME MECHANICS LETTERS,2024,71:13. |
| APA | 王志文,陈贤佳,温济慈,&魏宇杰.(2024).Determining plastic slips in rate-independent crystal plasticity models through machine learning algorithms.EXTREME MECHANICS LETTERS,71,13. |
| MLA | 王志文,et al."Determining plastic slips in rate-independent crystal plasticity models through machine learning algorithms".EXTREME MECHANICS LETTERS 71(2024):13. |
入库方式： OAI收割
来源：力学研究所
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